Distributed Regret Matching Algorithm for a Dynamic Route Guidance

2014 ◽  
pp. 107-116 ◽  
Author(s):  
Tai-Yu Ma
Keyword(s):  
Route Guidance ◽  
Matching Algorithm ◽  
Dynamic Route

scholarly journals Dynamic Guidance by Colored Running Lights and Affordance: Route Choices of Adults and Older Children

2020 ◽  
Vol 5 ◽  
Author(s):  
Laura Künzer ◽  
Robert Zinke ◽  
Gesine Hofinger
Keyword(s):  
Laboratory Experiment ◽  
Route Choice ◽  
The Other ◽  
Route Guidance ◽  
Older Children ◽  
Aversive Effect ◽  
Architectural Elements ◽  
Route Choices ◽  
Subway Stations ◽  
Dynamic Route

Abstract Guidance to emergency exits play an important role for safe evacuation. Dynamic route guidance by colored flashing lights and strobe lights at emergency exits has been tested [1–3], but the effects of dynamic lights to support route choices need to be determined in more detail. Also, the guidance effects of different colors need to be examined and the reaction of various groups of evacuees. The paper analyzes the effects of red and green running lights on route choice in subway stations comparing adults and older children (10 to 12 years old). Data was gathered in a laboratory experiment, focusing on the concept of affordance [4, 5]. Participants were asked to make a decision about the safest direction between two alternative directions. Their choice was either unsupported or supported by red or green running lights. In general, an interaction between color and direction of the running light was found. Green running lights influenced route choices of both participant groups and led participants clearly into the direction indicated by the lights. Red running lights influenced route choices of both participant groups, but red lights lead to ambiguous decisions. Architectural elements such as stairs influenced route choices of both participant groups (functional affordance). But green running light offered a stronger indication to a safe route (cognitive affordance) than a visible staircase (functional affordance). Green lights even led participants to modify their route preference. In contrast, red running lights had an aversive effect: older children chose against the lights and preferred the other direction than the red lights were directing to. Implications for design of dynamic route guidance are discussed. This includes colored running lights to lead evacuees to a safe exit and to implement the influence of running lights on route choice and movement in simulations.

Dynamic route guidance algorithm based on artificial immune system

2007 ◽  
Vol 5 (4) ◽  
pp. 385-390 ◽  
Author(s):  
Licai Yang ◽  
Jie Lin ◽  
Dewei Wang ◽  
Lei Jia
Keyword(s):  
Immune System ◽  
Artificial Immune System ◽  
Route Guidance ◽  
Artificial Immune ◽  
Guidance Algorithm ◽  
Dynamic Route

Optimization of Traffic Dynamic Route Guidance with Drivers, Reactions

1993 ◽  
Vol 26 (2) ◽  
pp. 149-152
Author(s):  
J. Weymann ◽  
J.-L. Farges ◽  
J.-J. Henry
Keyword(s):  
Route Guidance ◽  
Dynamic Route

Dynamic route guidance with queue- and flow-dependent travel time

1994 ◽  
Vol 2 (3) ◽  
pp. 165-183 ◽  
Author(s):  
J. Weymann ◽  
J.-L. Farges ◽  
J.-J. Henry
Keyword(s):  
Travel Time ◽  
Route Guidance ◽  
Dynamic Route

scholarly journals Hierarchical Sarsa Learning Based Route Guidance Algorithm

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Feng Wen ◽  
Xingqiao Wang ◽  
Xiaowei Xu
Keyword(s):  
State Space ◽  
Road Network ◽  
Large Scale ◽  
Road Networks ◽  
Modern Society ◽  
Guidance System ◽  
Route Guidance ◽  
Guidance Algorithm ◽  
Reinforcement Learning Models ◽  
Dynamic Route

In modern society, route guidance problems can be found everywhere. Reinforcement learning models can be normally used to solve such kind of problems; particularly, Sarsa Learning is suitable for tackling with dynamic route guidance problem. But how to solve the large state space of digital road network is a challenge for Sarsa Learning, which is very common due to the large scale of modern road network. In this study, the hierarchical Sarsa learning based route guidance algorithm (HSLRG) is proposed to guide vehicles in the large scale road network, in which, by decomposing the route guidance task, the state space of route guidance system can be reduced. In this method, Multilevel Network method is introduced, and Differential Evolution based clustering method is adopted to optimize the multilevel road network structure. The proposed algorithm was simulated with several different scale road networks; the experiment results show that, in the large scale road networks, the proposed method can greatly enhance the efficiency of the dynamic route guidance system.

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